Description
Description: School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland. Emissions of harmful substances into the atmosphere are a serious
environmental concern. In order to understand and predict their effects, it
is necessary to estimate the exact quantity and timing of the emissions from
sensor measurements taken at different locations. There are a number of
methods for solving this problem. However, these existing methods assume
Gaussian additive errors, making them extremely sensitive to outlier
measurements. We first show that the errors in real-world measurement
data sets come from a heavy-tailed distribution, i.e., include outliers.
Hence, we propose robustifying the existing inverse methods by adding a blind
outlier-detection algorithm. The improved performance of our method is
demonstrated on a real data set and compared to previously proposed methods.
For the blind outlier detection, we first use an existing algorithm, RANSAC,
and then propose a modification called TRANSAC, which provides a further
performance improvement.